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Candidate transcription factors and their target genes are selected from differentially expressed GEO data, then driver genes guide the discovery of regulatory microRNAs and LncRNAs. A co-expression network is reconstructed in R (WGCNA), module hub genes are ranked, TF–target relationships are mapped (TRRUST), biomarkers are evaluated (UCSC Xena, GraphPad), and pathway enrichment is performed (Enrichr, Gene Ontology/KEGG).",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/novel-bioinformatic-approaches-show-the-role-of-driver-genes-in-the-progression-of-cervical-cancer-an-in-silico-study/329577/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/novel-bioinformatic-approaches-show-the-role-of-driver-genes-in-the-progression-of-cervical-cancer-an-in-silico-study/329577.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s main objective regarding cervical cancer?","Question",{"text":112,"@type":113},"To identify driver genes and elucidate their roles in the earliest stages, development, progression, and potential diagnosis and treatment targets for cervical cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods and tools are used to build and analyze the gene networks?",{"text":117,"@type":113},"The study uses differentially expressed genes from GEO (GSE63514), constructs a co-expression network with WGCNA in R, identifies module hub genes with CytoHubba, and evaluates biomarkers using UCSC Xena and GraphPad Prism.",{"name":119,"@type":110,"acceptedAnswer":120},"How are transcription factors, non-coding RNAs, and drug interactions incorporated?",{"text":121,"@type":113},"Transcription factors regulating hub genes are located using TRRUST, regulatory interactions with microRNAs and LncRNAs are analyzed, and drug–target relationships are explored using DGIDB, miRWalk, and LncHub.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},329577,1790762309,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","Heliyon 10 (2024) e40179  \nContents lists available at ScienceDirect  \nHeliyon  \njournal [homepage:](homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](homepage: www.cell.com/heliyon)  \nResearch article  \nNovel bioinformatic approaches show the role of driver genes in the progression of cervical cancer: An in-silico study  \nAmir Hossein Yaria,b,1, Parisa Shiri Aghbash b,c,1, Mobina Bayatc,d, Shiva Lahoutib, Nazila Jalilzadehb, Leila Nariman Zadehb, Amir Mohammad Yaria,  \nParinaz Tabrizi-Nezhadia, Javid Sadri Nahand d, Habib MotieGhadera,e,**, Hossein Bannazadeh Baghib,c,d,*  \na Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran b Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran  \nc Department of Virology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran d Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran e Department of Software Engineering, Engineering Faculty, Istanbul Topkapi University, Istanbul, Turkey  \nA R T I C L E I N F O  \nKeywords: Bioinformatics Systems biology Transcription factors Driver genes WGCNA R package Cervical cancer  \nA B S T R A C T  \nBackground: The goal of this bioinformatics research is to get a comprehensive understanding of the driver genes and their function in the development, progression, and treatment of cervical cancer. This study constitutes a pioneering attempt, adding to our knowledge of genetic diversity and its ramifications.  \nMaterial and methods: In this project, we use bioinformatics and systems biology methods to identify candidate transcription factors and the genes they regulate in order to identify microRNAs and LncRNAs that regulate these transcription factors and lead to the discovery of new medicines for the treatment of cervical cancer. From the differentially expressed genes available via GEO’s GSE63514 accession, we use driver genes to choose these candidates. We then used the WGCNA tool in R to rebuild the co-expression network and its modules. The hub genes of each module were determined using CytoHubba, a Cystoscope plugin. The biomarker potential of hub genes was analyzed using the UCSC Xena browser and the GraphPad prism program. The TRRUST database is used to locate the TFs that regulate the expression of these genes. In order to learn how drugs, MicroRNAs, and LncRNAs interact with transcription factors, we consulted the Drug Target Information Database (DGIDB), the miRWalk database, and the LncHub database. Finally, the online database Enrichr is utilized to analyze the enrichment of Gene Ontology and KEGG pathways.  \nResults: By combining the mRNA expression levels of 2041 driver genes from 14 early-stage Cervical cancer and 24 control samples, a co-expression network was built. The cluster analysis shows that the collection of shared genes may be broken down into seven distinct groups, or\"modules.\" According to the average linkage hierarchical clustering and Summary smaller than 2, we found five modules (represented by the colors blue, brown, red, green, and grey) in our research. Then, we identify 5 high-degree genes from these modules that may serve as diagnostic  \n* Corresponding author. Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.  \n** Corresponding author. Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.  \n[E-mail addresses:](E-mail addresses: habib_moti@ut.ac.ir)[ habib_moti@ut.ac.ir](E-mail addresses: habib_moti@ut.ac.ir) (H. MotieGhader), [hb.zadeh@gmail.com](hb.zadeh@gmail.com) (H.B. Baghi).  \n1 Amir Hossein Yari and Parisa Shiri Aghbash have equally to this work.  \n[https://doi.org/10.1016/j.heliyon.2024.e40179](https://doi.org/10.1016/j.heliyon.2024.e40179)  \nReceived 12 February 2024; Received in revised form 31 October 2024; Accepted 5 November 2024 Available online 14 November 2024  \n2405-8440/© 2024 The Authors. Published by Elsevier Ltd. This is an open","cbCaivdq4eQfxm5O","https://ap.wps.com/l/cbCaivdq4eQfxm5O","pdf",7426639,17,"English","# Background\n# Materials and methods\n# Results\n## Co-expression network construction and modules\n## Hub genes, biomarkers, and transcription factors\n# Conclusion\n# Introduction","[{\"question\":\"What is the study’s main objective regarding cervical cancer?\",\"answer\":\"To identify driver genes and elucidate their roles in the earliest stages, development, progression, and potential diagnosis and treatment targets for cervical cancer.\"},{\"question\":\"Which methods and tools are used to build and analyze the gene networks?\",\"answer\":\"The study uses differentially expressed genes from GEO (GSE63514), constructs a co-expression network with WGCNA in R, identifies module hub genes with CytoHubba, and evaluates biomarkers using UCSC Xena and GraphPad Prism.\"},{\"question\":\"How are transcription factors, non-coding RNAs, and drug interactions incorporated?\",\"answer\":\"Transcription factors regulating hub genes are located using TRRUST, regulatory interactions with microRNAs and LncRNAs are analyzed, and drug–target relationships are explored using DGIDB, miRWalk, and LncHub.\"}]","Novel bioinformatic approaches show the role of driver genes in the progression of cervical cancer - An in-silico study | PDF",1789984600,43]